Many communication systems, such as radar systems, sonar systems and microphone arrays, use beamforming to enhance the reception of signals. In contrast to conventional communication systems that do not discriminate between signals based on the position of the signal source, beamforming systems are characterized by the capability of enhancing the reception of signals generated from sources at specific locations relative to the system.
Generally, beamforming systems include an array of spatially distributed sensor elements, such as antennas, sonar phones or microphones, and a data processing system for combining signals detected by the array. The data processor combines the signals to enhance the reception of signals from sources located at select locations relative to the sensor elements. Essentially, the data processor "aims" the sensor array in the direction of the signal source. For example, a linear microphone array uses two or more microphones to pick up the voice of a talker. Because one microphone is closer to the talker than the other microphone, there is a slight time delay between the two microphones. The data processor adds a time delay to the nearest microphone to coordinate these two microphones. By compensating for this time delay, the beamforming system enhances the reception of signals from the direction of the talker, and essentially aims the microphones at the talker.
A major factor in the effectiveness of these beamforming systems is the accuracy of the time delays necessary for aiming the sensor array. One known technique for determining the time delays necessary for aiming the sensor array employs a priori knowledge of the source position, the source orientation and the radiation pattern of the signal. Essentially, the data processor determines from the position of the source and, from the position of the sensor elements, a delay factor for each of the sensor elements. The data processor then applies such delay factors to the respective sensor elements to aim the sensor array in the direction of the signal source.
Although these systems work well if the position of the signal source is precisely known, the effectiveness of these systems drops off dramatically with slight errors in the estimated a priori information. For instance, in some systems with source-location schemes, it has been shown that the data processor must know the location of the source within a few centimeters to enhance the reception of signals. Therefore, these systems require precise knowledge of the position of the source, and precise knowledge of the position of the sensors. As a consequence, these systems require both that the sensor elements in the array have a known and static spatial distribution and that the signal source remains stationary relative to the sensor array. Furthermore, these beamforming systems require a first step for determining the talker position and a second step for aiming the sensor array based on the expected position of the talker.
Other techniques for determining the direction for aiming the sensor array rely on a priori information regarding the signal waveform and the signal radiation pattern. For example, radar systems use beamforming to transmit signals in a select direction. If an object is present in that direction, the signal reflects off the object and travels back toward the radar system. Therefore, the radar system is transmitting and receiving very similar signals. Furthermore, the data processor assumes that the objects are sufficiently distant from the sensor array that the incoming signals have a particular radiation pattern. The assumed radiation pattern can be a particularly simple pattern that reduces the complexity of the time delay computation.
The radar system capitalizes on the similarity of the transmitted and received signals by using signals that have features which facilitate signal processing. The data processor can directly compare the features of the received signal against the features of the transmitted signal and determine differences between the two signals that relate to the relative time delays between each sensor. Furthermore, the radar system can use the assumptions regarding the radiation pattern of the incoming signals to simplify the signal processing techniques necessary to calculate the time delays. The data processor then compensates for the respective time delays between each sensor element to aim the sensor array in the direction of the object.
Although these systems work well if the signal waveform is known, these systems less effective where the a priori information regarding the signal waveform is unavailable or insufficient to allow the received signals to be compared against a known signal waveform. Therefore, these systems are generally limited to active systems that both transmit and receive signals. Furthermore, these systems are less effective when assumptions regarding the radiation pattern cannot be made. Therefore, these systems are usually limited to those applications where the signal source is sufficiently distant from the sensor array that a signal pattern can be assumed.
A known technique for determining the direction of incoming signals without a priori information employs correlation strategies that compare signals received by the array at spatially distinct sensors to estimate the time delays between the sensors. The time delay information, along with assumptions about the radiation pattern, are used to estimate the location of the signal source. One example of correlation strategies for locating talker position with a microphone array in a near-field environment is set forth in Silverman et al., A Two-Stage Algorithm for Determining Talker Location from Linear Microphone Array Data, Computer Speech and Language, at 129-152 (1992). In general, the cross-correlation function of two signals received at two distinct sensors is computed and filtered in some optimal sense. The data processor includes a peak detector that detects the maximum value of the filtered signal. While the filtering criteria and the methods used for peak detection may vary considerably, these techniques are all based on maximizing the correlation between two received signals and determining from the detected peak the relative time delays between the associated sensors. Once the time delays are determined, techniques, such as triangulation, can be used to determine the location of the signal source.
Although these systems can work well, there is generally a trade-off between the accuracy of the time delay estimate and the computational expense incurred by the procedure. Furthermore, there can be a tradeoff between the accuracy of the delay estimate and the rate at which the system can acquire the incoming signals. The cross-correlation function is a computationally intensive operation, and the accuracy of the peak data increases with the number of comparisons made during the correlation. In order to achieve a peak that is sufficiently accurate and well defined to identify precisely the position of the source, the computational burden can be prohibitive. Therefore, these systems can fail to produce the desired accuracy and update rate required for effective beamforming in a real-time environment.
In view of the foregoing, an object of the present invention is to provide improved signal processing methods and systems for combining a plurality of signals, and more particularly, to provide improved systems and methods for beamforming that dynamically determine the time delay estimates for a sensor array as part of the beamforming process.
A further object of the present invention is to provide systems and methods for real-time beamforming without the need of a priori information about the position of the signal source or knowledge of the signal radiation pattern.
Another object of the present invention is to provide signal processing systems and methods for adaptively aiming an array of sensor elements at a moving signal source.
A yet further object of the present invention is to provide signal processing systems and methods that can dynamically compensate for a sensor array that has a non-uniform or unknown spatial distribution of sensors.
A still further object of the present invention is to provide systems and methods for real-time beamforming without the need of a priori information about the signal waveform.
Still another object of the present invention is to provide computationally efficient systems and methods to determine the relative time delays between the signals received by the sensor elements of a sensor array and employ these delay estimates for computationally efficient beamforming and source location.
These and other objects of the invention are evident in the sections that follow.